Introduction

As a PhD student affiliated with CSIRO’s Oceans and Atmosphere Business Unit, I conduct research on marine debris, more specifically on solutions that can be utilised to prevent marine debris and improve the governance of this problem. Having just recently started my PhD candidature and having never coded before, my participation in the Data School FOCUS is an incredible opportunity. The skillsets taught during the Data School FOCUS are directly relevant for my PhD project.

My Project

As part of my PhD research, I will use ‘R’ to assess different data provided by the nonprofit advocacy group Ocean Conservancy to examine the effectiveness of different policies and practices to reduce marine debris entering the oceans. The datasets that I use throughout the Data School FOCUS consists of about 50,000 entries in different excel files. Each entry is a marine debris survey undertaken by volunteers who submitted their data to Ocean Conservancy (from the years 2011 until 2018. I had not yet done any analysis with the data prior to my participation in Data School FOCUS because knowledge of ‘R’ is essential for this analysis. That is why the participation in the Data School FOCUS is of immense benefit for my research.

Rather than yourself, this is the space to introduce your project. What are your goals, what was your data, how do you plan to work with it? Perhaps show some example data if it would help.

In order to build this demo poster correctly, you will also need to have installed the tidyverse, gapminder, and kableExtra packages.

Preliminary results

This section will demonstrate the different visuals you might want use to show off your project. Don’t feel the need to go overboard, this is supposed to give a taste of the work you are doing rather than being a publication ready document.

To get tables formatting correctly, use knitr::kable to convert the table to html format. If you also want to have alternate row highlighting, pass the result to kable_styling('striped') from the kableExtra package.

Tables

knitr::kable(head(gapminder, n = 5), format = "html", caption = "A table of data") %>% 
  kable_styling("striped")

Table 1: A table of data

country	continent	year	lifeExp	pop	gdpPercap
Afghanistan	Asia	1952	28.801	8425333	779.4453
Afghanistan	Asia	1957	30.332	9240934	820.8530
Afghanistan	Asia	1962	31.997	10267083	853.1007
Afghanistan	Asia	1967	34.020	11537966	836.1971
Afghanistan	Asia	1972	36.088	13079460	739.9811

Images from a file

Plots from R

Figure 1: Yet another gapminder plot

Your figure and table captions are automatically numbered and can be referenced in the text if needed: see eg. Table 1 and Figure 1

My Digital Toolbox

As I had not yet had the chance to formally learn about ‘R’ prior to my participation in the Data School FOCUS, acquiring skills in the different R software packages was an unique and well-timed opportunity for me.

What digital tools have you been using in your project? Which ones have you learned since starting Data School?

You can use all the usual R markdown features in writing a project summary, including lists:

• R - dplyr, ggplot, …
• Python
• SQL

My time went …

The tidying of my data took much longer time and effort than expected. Applying the skills I learned in Data School FOCUS on best-practice data analysis, visualisation and management in the future will help me to save time and manage my data more effectively.

What parts of the project took the most time and effort? Were there any surprising challenges you encountered, and how did you solve them?

Next steps

I would be interested in learning more about gganimate.

What further steps do you wish your project could take? Or are there any new digital skills that you are keen to develop as a result of your involvement in the Data School?

My Data School Experience

The possibility to establish fundamental programming and data analysis skills through the use of ‘R’ is immensely beneficial for my PhD research and professional development. Having just recently started my PhD candidature and having had no experience in programming or using ‘R’, the timing of the Data School FOCUS could not have been better. I started exploring ‘R’ before the Data School FOCUS. My participation in the Data School FOCUS helped me to understand and effectively apply ‘R’ and the associated R software packages to address my research questions. Following my participation in Data School FOCUS, I will apply the skills I learned for my analysis which will take my PhD research to the enxt level. These skills will enable me to kick-start my PhD research and the associated analytical skills I need to succeed as a research scientist.

This poster is mostly about your synthesis project. However we would also like to hear about other parts of your Data School experience. What aspects of the program did you really enjoy? How have you been applying the skills you have learned in your daily work? Have you been able to transfer this knowledge to your team members? Concrete examples demonstrating this would be useful here (meetings/talks/collaborations/new roles). Any descriptions of the personal impact the program has had are welcome here as well!